Sensitivity Analysis¶

In [29]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import os
import glob
import re
from cycler import cycler

import fiona
import geopandas as gp
import matplotlib.pyplot as plt
import pandas as pd
from matplotlib import collections
import matplotlib.pyplot as plt
from shapely.geometry import LineString, Point, Polygon

%matplotlib inline

sns.color_palette()

sns.set_style("white", {"xtick.direction": "in","ytick.direction": "in"})
plt.rcParams['xtick.bottom'] = True
plt.rcParams['ytick.left'] = True

Setup all file scenarios¶

The files I am going to load are the ones showed in the table below. This is to know which file correspond to what. I have not added the dates in the file name since they are autogenerated and it is a sanity check for me, but in reality the dates (numbers in front of the file name scenario) don't matter. scenarios_info.png scenarios_info_cap.png

In [2]:
cwd = os.getcwd()
In [3]:
facility_label = ['Manufacturing', 'Recycling', 'Manufacturing_cap', 'Recycling_cap']
location_label = ['NAICS', '40209']
factor_label_rec = ['05', '1', '2', '5', '10']
factor_label_man = ['0001','05', '1', '2'] # 0 is 0,5
In [4]:
files_list = []
for fac in facility_label:
    for loc in location_label:
        if fac.startswith('Manufacturing'):
            for fac_man in factor_label_man:
                files_list.append(fac+'_'+loc+'_'+fac_man)
        else:
            for fac_rec in factor_label_rec:
                files_list.append(fac+'_'+loc+'_'+fac_rec)

Group the files into smaller bins for easier handling¶

In [5]:
recycling_files = [x for x in files_list if x.startswith('Recycling')]
recycling_files_cap = [x for x in recycling_files if "cap" in x]
recycling_files_cost = [x for x in recycling_files if "cap" not in x]
In [6]:
manufacturing_files = [x for x in files_list if x.startswith('Manufacturing')]
manufacturing_files_cap = [x for x in manufacturing_files if "cap" in x]
manufacturing_files_cost = [x for x in manufacturing_files if "cap" not in x]

Dictionary to change year number to actual years¶

In [7]:
year_list = list(range(2025, 2051, 1))
number_year = list(range(1, 27, 1))
years_dict = dict(zip(number_year,year_list))

Load plant files¶

Recycling cost¶
In [8]:
for files in recycling_files_cost:
    testfile_path = glob.glob(os.path.join(cwd, "scenarios_after_update", files, 'plants.csv'))[0]
    globals()['%s_plants' % (files)] = pd.read_csv(testfile_path) 
    globals()['%s_plants' % (files)] = globals()['%s_plants' % (files)].replace({"year": years_dict})
    print('{}: {} locations'.format(files, len(globals()['%s_plants' % (files)]['location name'].unique())))

Recycling_NAICS_05: 81 locations
Recycling_NAICS_1: 81 locations
Recycling_NAICS_2: 81 locations
Recycling_NAICS_5: 81 locations
Recycling_NAICS_10: 81 locations
Recycling_40209_05: 81 locations
Recycling_40209_1: 81 locations
Recycling_40209_2: 81 locations
Recycling_40209_5: 81 locations
Recycling_40209_10: 81 locations

Some of the manufacturing files have no solution, so we remove them:

Manufacturing cost¶
In [9]:
for files in manufacturing_files_cost:
    testfile_path = glob.glob(os.path.join(cwd, "scenarios_after_update", files, 'plants.csv'))[0]
    globals()['%s_plants' % (files)] = pd.read_csv(testfile_path) 
    globals()['%s_plants' % (files)] = globals()['%s_plants' % (files)].replace({"year": years_dict})
    print('{}: {} locations'.format(files, len(globals()['%s_plants' % (files)]['location name'].unique())))

Manufacturing_NAICS_0001: 187 locations
Manufacturing_NAICS_05: 166 locations
Manufacturing_NAICS_1: 166 locations
Manufacturing_NAICS_2: 166 locations
Manufacturing_40209_0001: 177 locations
Manufacturing_40209_05: 166 locations
Manufacturing_40209_1: 166 locations
Manufacturing_40209_2: 166 locations
Recycling capacity¶
In [10]:
for files in recycling_files_cap:
    testfile_path = glob.glob(os.path.join(cwd, "scenarios_after_update", files, 'plants.csv'))[0]
    globals()['%s_plants' % (files)] = pd.read_csv(testfile_path)
    globals()['%s_plants' % (files)] = globals()['%s_plants' % (files)].replace({"year": years_dict})
    print('{}: {} locations'.format(files, len(globals()['%s_plants' % (files)]['location name'].unique())))    

Recycling_cap_NAICS_05: 161 locations
Recycling_cap_NAICS_1: 81 locations
Recycling_cap_NAICS_2: 41 locations
Recycling_cap_NAICS_5: 17 locations
Recycling_cap_NAICS_10: 9 locations
Recycling_cap_40209_05: 161 locations
Recycling_cap_40209_1: 81 locations
Recycling_cap_40209_2: 41 locations
Recycling_cap_40209_5: 17 locations
Recycling_cap_40209_10: 9 locations
Manufacturing capacity¶
In [11]:
manufacturing_files_cap.remove('Manufacturing_cap_NAICS_0001')
manufacturing_files_cap.remove('Manufacturing_cap_NAICS_05') 
manufacturing_files_cap.remove('Manufacturing_cap_40209_0001')
manufacturing_files_cap.remove('Manufacturing_cap_40209_05')
In [12]:
for files in manufacturing_files_cap:
    testfile_path = glob.glob(os.path.join(cwd, "scenarios_after_update", files, 'plants.csv'))[0]
    globals()['%s_plants' % (files)] = pd.read_csv(testfile_path)
    globals()['%s_plants' % (files)] = globals()['%s_plants' % (files)].replace({"year": years_dict})
    print('{}: {} locations'.format(files, len(globals()['%s_plants' % (files)]['location name'].unique())))    

Manufacturing_cap_NAICS_1: 166 locations
Manufacturing_cap_NAICS_2: 83 locations
Manufacturing_cap_40209_1: 166 locations
Manufacturing_cap_40209_2: 83 locations

Selected locations plot¶

In [15]:
rec_cap_locs_data = [['Capacity', 'Recycling', 'NAICS', 0.5, 161], 
                     ['Capacity', 'Recycling','NAICS',1, 81], 
                     ['Capacity', 'Recycling','NAICS',2, 41],
                     ['Capacity', 'Recycling','NAICS',5, 17],
                     ['Capacity', 'Recycling','NAICS',10, 9],
                     ['Capacity', 'Recycling', '40209', 0.5, 161], 
                     ['Capacity', 'Recycling','40209',1, 81], 
                     ['Capacity', 'Recycling','40209',2, 41],
                     ['Capacity', 'Recycling','40209',5, 17],
                     ['Capacity', 'Recycling','40209',10, 9],
                     ['Cost', 'Recycling', 'NAICS',0.5, 81], 
                     ['Cost', 'Recycling','NAICS',1, 81], 
                     ['Cost', 'Recycling','NAICS',2, 81],
                     ['Cost', 'Recycling','NAICS',5, 81],
                     ['Cost', 'Recycling','NAICS',10, 81],
                     ['Cost', 'Recycling', '40209',0.5, 81], 
                     ['Cost', 'Recycling','40209',1, 81], 
                     ['Cost', 'Recycling','40209',2, 81],
                     ['Cost', 'Recycling','40209',5, 81],
                     ['Cost', 'Recycling','40209',10, 81],
                     ['Capacity', 'Manufacturing', 'NAICS',1, 166],
                     ['Capacity', 'Manufacturing', 'NAICS',2, 83],
                     ['Capacity', 'Manufacturing', '40209', 1, 166],
                     ['Capacity', 'Manufacturing', '40209', 2, 83],
                     ['Cost', 'Manufacturing', 'NAICS',0.001, 187],
                     ['Cost', 'Manufacturing', 'NAICS',0.5, 166],
                     ['Cost', 'Manufacturing', 'NAICS',1, 166],
                     ['Cost', 'Manufacturing', 'NAICS',2, 166],
                     ['Cost', 'Manufacturing', '40209',0.001, 177],
                     ['Cost', 'Manufacturing', '40209',0.5, 166],
                     ['Cost', 'Manufacturing', '40209',1, 166],
                     ['Cost', 'Manufacturing', '40209',2, 166]]

rec_cap_locs_data_df = pd.DataFrame(rec_cap_locs_data, columns=['Analysis', 'Facility', 'Location group','Factor', 'Selected locations'])
In [16]:
rec_cap_locs_data_df.loc[rec_cap_locs_data_df['Facility'] == 'Recycling'  ]
Out[16]:
Analysis Facility Location group Factor Selected locations
0 Capacity Recycling NAICS 0.5 161
1 Capacity Recycling NAICS 1.0 81
2 Capacity Recycling NAICS 2.0 41
3 Capacity Recycling NAICS 5.0 17
4 Capacity Recycling NAICS 10.0 9
5 Capacity Recycling 40209 0.5 161
6 Capacity Recycling 40209 1.0 81
7 Capacity Recycling 40209 2.0 41
8 Capacity Recycling 40209 5.0 17
9 Capacity Recycling 40209 10.0 9
10 Cost Recycling NAICS 0.5 81
11 Cost Recycling NAICS 1.0 81
12 Cost Recycling NAICS 2.0 81
13 Cost Recycling NAICS 5.0 81
14 Cost Recycling NAICS 10.0 81
15 Cost Recycling 40209 0.5 81
16 Cost Recycling 40209 1.0 81
17 Cost Recycling 40209 2.0 81
18 Cost Recycling 40209 5.0 81
19 Cost Recycling 40209 10.0 81

Recycling¶

In [17]:
figure = sns.lineplot(x='Factor', y='Selected locations', data=rec_cap_locs_data_df.loc[rec_cap_locs_data_df['Facility'] == 'Recycling'], marker='o', hue='Analysis', style="Analysis", )
figure.legend(frameon=False)
figure.set(title='Recycling')
Out[17]:
[Text(0.5, 1.0, 'Recycling')]

Manufacturing¶

In [31]:
figure = sns.lineplot(x='Factor', y='Selected locations', data=rec_cap_locs_data_df.loc[rec_cap_locs_data_df['Facility'] == 'Manufacturing'], marker='o', hue='Analysis', style="Analysis", )
figure.legend(frameon=False)
figure.set(title='Manufacturing')
Out[31]:
[Text(0.5, 1.0, 'Manufacturing')]

Grid plot¶

Recycling Capacity Utilization Factor¶

In [46]:
for files in recycling_files_cap:
# Initialize a grid of plots with an Axes for each walk
    grid = sns.FacetGrid(globals()['%s_plants' % (files)], col="location name",
                         col_wrap=9, height=1.5)

    # Draw a horizontal line to show the starting point
    grid.refline(y=0, linestyle=":")


    grid.map(plt.plot, "year", "utilization factor (%)", color="powderblue")
    grid.map(plt.fill_between, 'year', 'utilization factor (%)',color= "powderblue", alpha= 0.2)
    # Adjust the tick positions, labels and 
    #sns.set(font_scale=0.1)
    grid.set_titles(row_template = '{row_name}', col_template = '{col_name}', size=5)

    grid.fig.tight_layout(w_pad=1)
    grid.fig.subplots_adjust(top=0.9)
    grid.fig.suptitle(f"{files}")


    plt.savefig(os.path.join(cwd, f"grid_after_update/grid_{files}_cap_uf.png"), transparent=True);

Recycling Capacity Total Cost¶

In [ ]:
for files in recycling_files_cap:
# Initialize a grid of plots with an Axes for each walk
    grid = sns.FacetGrid(globals()['%s_plants' % (files)], col="location name",
                         col_wrap=9, height=1.5)

    # Draw a horizontal line to show the starting point
    grid.refline(y=0, linestyle=":")


    grid.map(plt.plot, "year", "total cost ($)", color="powderblue")
    grid.map(plt.fill_between, 'year', 'total cost ($)',color= "powderblue", alpha= 0.2)
    # Adjust the tick positions, labels and 
    #sns.set(font_scale=0.1)
    grid.set_titles(row_template = '{row_name}', col_template = '{col_name}', size=5)

    grid.fig.tight_layout(w_pad=1)
    grid.fig.subplots_adjust(top=0.9)
    grid.fig.suptitle(f"{files}")


    plt.savefig(os.path.join(cwd, f"grid_after_update/grid_{files}_cap_totcost.png"), transparent=True);
In [ ]:

Recycling Cost Utilization Factor¶

In [48]:
for files in recycling_files_cost:
# Initialize a grid of plots with an Axes for each walk
# Initialize a grid of plots with an Axes for each walk
    grid = sns.FacetGrid(globals()['%s_plants' % (files)], col="location name",
                         col_wrap=9, height=1.5)

    # Draw a horizontal line to show the starting point
    grid.refline(y=0, linestyle=":")


    grid.map(plt.plot, "year", "utilization factor (%)", color="powderblue")
    grid.map(plt.fill_between, "year", 'utilization factor (%)',color= "powderblue", alpha= 0.2)
    # Adjust the tick positions, labels and 
    #sns.set(font_scale=0.1)
    grid.set_titles(row_template = '{row_name}', col_template = '{col_name}', size=5)

    grid.fig.tight_layout(w_pad=1)
    grid.fig.subplots_adjust(top=0.9)
    grid.fig.suptitle(f"{files}")


    plt.savefig(os.path.join(cwd, f"grid_after_update/grid_{files}_cost_uf.png"), transparent=True);
In [ ]:

Recycling Cost Total Cost¶

In [35]:
for files in recycling_files_cost:
# Initialize a grid of plots with an Axes for each walk
    grid = sns.FacetGrid(globals()['%s_plants' % (files)], col="location name",
                         col_wrap=9, height=1.5)

    # Draw a horizontal line to show the starting point
    grid.refline(y=0, linestyle=":")


    grid.map(plt.plot, "year", "total cost ($)", color="powderblue")
    grid.map(plt.fill_between, 'year', 'total cost ($)',color= "powderblue", alpha= 0.2)
    # Adjust the tick positions, labels and 
    #sns.set(font_scale=0.1)
    grid.set_titles(row_template = '{row_name}', col_template = '{col_name}', size=5)

    grid.fig.tight_layout(w_pad=1)
    grid.fig.subplots_adjust(top=0.9)
    grid.fig.suptitle(f"{files}")


    plt.savefig(os.path.join(cwd, f"grid_after_update/grid_{files}_cost_totcost.png"), transparent=True);
In [ ]:

Manufacturing Capacity Utilization Factor¶

In [49]:
for files in manufacturing_files_cap:
# Initialize a grid of plots with an Axes for each walk
    grid = sns.FacetGrid(globals()['%s_plants' % (files)], col="location name",
                         col_wrap=9, height=1.5)

    # Draw a horizontal line to show the starting point
    grid.refline(y=0, linestyle=":")


    grid.map(plt.plot, "year", "utilization factor (%)", color='gold')
    grid.map(plt.fill_between, 'year', 'utilization factor (%)',color= "gold", alpha= 0.2)
    # Adjust the tick positions, labels and 
    #sns.set(font_scale=0.1)
    grid.set_titles(row_template = '{row_name}', col_template = '{col_name}', size=5)

    grid.fig.tight_layout(w_pad=1)
    grid.fig.subplots_adjust(top=0.9)
    grid.fig.suptitle(f"{files}")


    plt.savefig(os.path.join(cwd, f"grid_after_update/grid_{files}_cap_uf.png"), transparent=True);

Manufacturing Capacity Total Cost¶

In [50]:
for files in manufacturing_files_cap:
# Initialize a grid of plots with an Axes for each walk
    grid = sns.FacetGrid(globals()['%s_plants' % (files)], col="location name",
                         col_wrap=9, height=1.5)

    # Draw a horizontal line to show the starting point
    grid.refline(y=0, linestyle=":")


    grid.map(plt.plot, "year", "total cost ($)", color='gold')
    grid.map(plt.fill_between, 'year', 'total cost ($)',color= "gold", alpha= 0.2)

    # Adjust the tick positions, labels and 
    #sns.set(font_scale=0.1)
    grid.set_titles(row_template = '{row_name}', col_template = '{col_name}', size=5)

    grid.fig.tight_layout(w_pad=1)
    grid.fig.subplots_adjust(top=0.9)
    grid.fig.suptitle(f"{files}")


    plt.savefig(os.path.join(cwd, f"grid_after_update/grid_{files}_cap_totcost.png"), transparent=True);
In [ ]:

Manufacturing Cost Utilization Factor¶

In [51]:
for files in manufacturing_files_cost:
# Initialize a grid of plots with an Axes for each walk
# Initialize a grid of plots with an Axes for each walk
    grid = sns.FacetGrid(globals()['%s_plants' % (files)], col="location name",
                         col_wrap=9, height=1.5)

    # Draw a horizontal line to show the starting point
    grid.refline(y=0, linestyle=":")


    grid.map(plt.plot, "year", "utilization factor (%)", color='gold')
    grid.map(plt.fill_between, 'year', 'utilization factor (%)',color= "gold", alpha= 0.2)

    # Adjust the tick positions, labels and 
    #sns.set(font_scale=0.1)
    grid.set_titles(row_template = '{row_name}', col_template = '{col_name}', size=5)

    grid.fig.tight_layout(w_pad=1)
    grid.fig.subplots_adjust(top=0.9)
    grid.fig.suptitle(f"{files}")


    plt.savefig(os.path.join(cwd, f"grid_after_update/grid_{files}_cost_uf.png"), transparent=True);
In [ ]:

Manufacturing Cost Total Cost¶

In [52]:
for files in manufacturing_files_cost:
# Initialize a grid of plots with an Axes for each walk
    grid = sns.FacetGrid(globals()['%s_plants' % (files)], col="location name",
                         col_wrap=9, height=1.5)

    # Draw a horizontal line to show the starting point
    grid.refline(y=0, linestyle=":")


    grid.map(plt.plot, "year", "total cost ($)", color='gold')
    grid.map(plt.fill_between, 'year', 'total cost ($)',color= "gold", alpha= 0.2)

    # Adjust the tick positions, labels and 
    #sns.set(font_scale=0.1)
    grid.set_titles(row_template = '{row_name}', col_template = '{col_name}', size=5)

    grid.fig.tight_layout(w_pad=1)
    grid.fig.subplots_adjust(top=0.9)
    grid.fig.suptitle(f"{files}")


    plt.savefig(os.path.join(cwd, f"grid_after_update/grid_{files}_cost_totcost.png"), transparent=True);
In [ ]:
In [ ]:

Map plots¶

USA map source2.

In [40]:
import fiona
import geopandas as gp
import matplotlib.pyplot as plt
import pandas as pd
from matplotlib import collections
import matplotlib.pyplot as plt
from shapely.geometry import LineString, Point, Polygon

%matplotlib inline

Load transportation files¶

Recycling cost¶
In [41]:
for files in recycling_files_cost:
    testfile_path = glob.glob(os.path.join(cwd, "scenarios_after_update", files, 'transportation.csv'))[0]
    globals()['%s_transportation' % (files)] = pd.read_csv(testfile_path) 
    globals()['%s_transportation' % (files)] = globals()['%s_transportation' % (files)].replace({"year": years_dict})
Recycling capacity¶
In [42]:
for files in recycling_files_cap:
    testfile_path = glob.glob(os.path.join(cwd, "scenarios_after_update", files, 'transportation.csv'))[0]
    globals()['%s_transportation' % (files)] = pd.read_csv(testfile_path) 
    globals()['%s_transportation' % (files)] = globals()['%s_transportation' % (files)].replace({"year": years_dict})
Manufacturing cost¶
In [43]:
for files in manufacturing_files_cost:
    testfile_path = glob.glob(os.path.join(cwd, "scenarios_after_update", files, 'transportation.csv'))[0]
    globals()['%s_transportation' % (files)] = pd.read_csv(testfile_path) 
    globals()['%s_transportation' % (files)] = globals()['%s_transportation' % (files)].replace({"year": years_dict})
Manufacturing capacity¶
In [44]:
for files in manufacturing_files_cap:
    testfile_path = glob.glob(os.path.join(cwd, "scenarios_after_update", files, 'transportation.csv'))[0]
    globals()['%s_transportation' % (files)] = pd.read_csv(testfile_path) 
    globals()['%s_transportation' % (files)] = globals()['%s_transportation' % (files)].replace({"year": years_dict})

Recycling cost maps¶

In [53]:
for files in recycling_files_cost:    
    # Plot base map
    world = gp.read_file(os.path.join(f'resources','USA_States_(Generalized)', 'USA_States_Generalized.shp'))
    world = world.to_crs("EPSG:4326")
    ax = world.plot(color="powderblue", edgecolor="1", figsize=(14, 7))
    ax.set_ylim([23, 50])
    ax.set_xlim([-128, -65])
    plt.axis('off')
    ax.set(title=f'{files}')
    # Draw transportation lines
    data = globals()['%s_transportation' % (files)]
    lines = [
        [
            (
                row["source longitude (deg)"],
                row["source latitude (deg)"],
            ),
            (
                row["destination longitude (deg)"],
                row["destination latitude (deg)"],
            ),
        ]
        for (index, row) in data.iterrows()
    ]
    ax.add_collection(
        collections.LineCollection(
            lines,
            linewidths=0.005,
            zorder=1,
            alpha=0.3,
            color="0.8",
        )
    )

    # Draw source points
    points = gp.points_from_xy(
        data["source longitude (deg)"],
        data["source latitude (deg)"],
    )
    gp.GeoDataFrame(data, geometry=points).plot(ax=ax, color="0.6", markersize=1)

    # Draw destination points
    points = gp.points_from_xy(
        data["destination longitude (deg)"],
        data["destination latitude (deg)"],
    )
    gp.GeoDataFrame(data, geometry=points).plot(ax=ax, markersize=50, color="palevioletred", edgecolor='white')
    
    plt.savefig(os.path.join("maps_after_update",f"map_{files}_cost.png"), transparent=True, dpi=300);
    plt.savefig(os.path.join("maps_after_update",f"map_{files}_cost.pdf"), dpi=300);

Recycling capacity maps¶

In [54]:
for files in recycling_files_cap:    
    # Plot base map
    world = gp.read_file(os.path.join(f'resources','USA_States_(Generalized)', 'USA_States_Generalized.shp'))
    world = world.to_crs("EPSG:4326")
    ax = world.plot(color="powderblue", edgecolor="1", figsize=(14, 7))
    ax.set_ylim([23, 50])
    ax.set_xlim([-128, -65])
    plt.axis('off')
    ax.set(title=f'{files}')
    # Draw transportation lines
    data = globals()['%s_transportation' % (files)]
    lines = [
        [
            (
                row["source longitude (deg)"],
                row["source latitude (deg)"],
            ),
            (
                row["destination longitude (deg)"],
                row["destination latitude (deg)"],
            ),
        ]
        for (index, row) in data.iterrows()
    ]
    ax.add_collection(
        collections.LineCollection(
            lines,
            linewidths=0.005,
            zorder=1,
            alpha=0.3,
            color="0.8",
        )
    )

    # Draw source points
    points = gp.points_from_xy(
        data["source longitude (deg)"],
        data["source latitude (deg)"],
    )
    gp.GeoDataFrame(data, geometry=points).plot(ax=ax, color="0.6", markersize=1)

    # Draw destination points
    points = gp.points_from_xy(
        data["destination longitude (deg)"],
        data["destination latitude (deg)"],
    )
    gp.GeoDataFrame(data, geometry=points).plot(ax=ax, markersize=50, color="palevioletred", edgecolor='white')
    
    plt.savefig(os.path.join("maps_after_update",f"map_{files}_cap.png"), transparent=True, dpi=300);
    plt.savefig(os.path.join("maps_after_update",f"map_{files}_cap.pdf"), dpi=300);

Manufacturing cost maps¶

In [55]:
for files in manufacturing_files_cost:    
    # Plot base map
    world = gp.read_file(os.path.join(f'resources','USA_States_(Generalized)', 'USA_States_Generalized.shp'))
    world = world.to_crs("EPSG:4326")
    ax = world.plot(color="gold", edgecolor="1", figsize=(14, 7))
    ax.set_ylim([23, 50])
    ax.set_xlim([-128, -65])
    plt.axis('off')
    ax.set(title=f'{files}')
    # Draw transportation lines
    data = globals()['%s_transportation' % (files)]
    lines = [
        [
            (
                row["source longitude (deg)"],
                row["source latitude (deg)"],
            ),
            (
                row["destination longitude (deg)"],
                row["destination latitude (deg)"],
            ),
        ]
        for (index, row) in data.iterrows()
    ]
    ax.add_collection(
        collections.LineCollection(
            lines,
            linewidths=0.005,
            zorder=1,
            alpha=0.3,
            color="0.8",
        )
    )

    # Draw source points
    points = gp.points_from_xy(
        data["source longitude (deg)"],
        data["source latitude (deg)"],
    )
    gp.GeoDataFrame(data, geometry=points).plot(ax=ax, color="0.6", markersize=1)

    # Draw destination points
    points = gp.points_from_xy(
        data["destination longitude (deg)"],
        data["destination latitude (deg)"],
    )
    gp.GeoDataFrame(data, geometry=points).plot(ax=ax, markersize=50, color="mediumpurple", edgecolor='white')
    
    plt.savefig(os.path.join("maps_after_update",f"map_{files}_cost.png"), transparent=True, dpi=300);
    plt.savefig(os.path.join("maps_after_update",f"map_{files}_cost.pdf"), dpi=300);

Manufacturing capacity maps¶

In [56]:
for files in manufacturing_files_cap:    
    # Plot base map
    world = gp.read_file(os.path.join(f'resources','USA_States_(Generalized)', 'USA_States_Generalized.shp'))
    world = world.to_crs("EPSG:4326")
    ax = world.plot(color="gold", edgecolor="1", figsize=(14, 7))
    ax.set_ylim([23, 50])
    ax.set_xlim([-128, -65])
    plt.axis('off')
    ax.set(title=f'{files}')
    # Draw transportation lines
    data = globals()['%s_transportation' % (files)]
    lines = [
        [
            (
                row["source longitude (deg)"],
                row["source latitude (deg)"],
            ),
            (
                row["destination longitude (deg)"],
                row["destination latitude (deg)"],
            ),
        ]
        for (index, row) in data.iterrows()
    ]
    ax.add_collection(
        collections.LineCollection(
            lines,
            linewidths=0.005,
            zorder=1,
            alpha=0.3,
            color="0.8",
        )
    )

    # Draw source points
    points = gp.points_from_xy(
        data["source longitude (deg)"],
        data["source latitude (deg)"],
    )
    gp.GeoDataFrame(data, geometry=points).plot(ax=ax, color="0.6", markersize=1)

    # Draw destination points
    points = gp.points_from_xy(
        data["destination longitude (deg)"],
        data["destination latitude (deg)"],
    )
    gp.GeoDataFrame(data, geometry=points).plot(ax=ax, markersize=50, color="mediumpurple", edgecolor='white')
    
    plt.savefig(os.path.join("maps_after_update",f"map_{files}_cap.png"), transparent=True, dpi=300);
    plt.savefig(os.path.join("maps_after_update",f"map_{files}_cap.pdf"), dpi=300);

Testing plots¶

In [67]:
# Plot base map
world = gp.read_file(os.path.join(f'resources','USA_States_(Generalized)', 'USA_States_Generalized.shp'))
world = world.to_crs("EPSG:4326")
ax = world.plot(color="gold", edgecolor="1", figsize=(14, 7))
ax.set_ylim([23, 50])
ax.set_xlim([-128, -65])
plt.axis('off')
ax.set(title=f'Recycling_40209_1_transportation')
# Draw transportation lines
data = Recycling_40209_1_transportation
lines = [
    [
        (
            row["source longitude (deg)"],
            row["source latitude (deg)"],
        ),
        (
            row["destination longitude (deg)"],
            row["destination latitude (deg)"],
        ),
    ]
    for (index, row) in data.iterrows()
]
ax.add_collection(
    collections.LineCollection(
        lines,
        linewidths=0.005,
        zorder=1,
        alpha=0.3,
        color="0.8",
    )
)

# Draw source points
points = gp.points_from_xy(
    data["source longitude (deg)"],
    data["source latitude (deg)"],
)
gp.GeoDataFrame(data, geometry=points).plot(ax=ax, color="0.6", markersize=1)

# Draw destination points
points = gp.points_from_xy(
    data["destination longitude (deg)"],
    data["destination latitude (deg)"],
)
gp.GeoDataFrame(data, geometry=points).plot(ax=ax, markersize=50, color="mediumpurple", edgecolor='white')
Out[67]:
<AxesSubplot:title={'center':'Recycling_40209_1_transportation'}>
In [68]:
world = gp.read_file(os.path.join(f'resources','USA_States_(Generalized)', 'USA_States_Generalized.shp'))
world = world.to_crs("EPSG:4326")
ax = world.plot(color="gold", edgecolor="1")
ax.set_ylim([23, 50])
ax.set_xlim([-128, -65])
plt.axis('off')
Out[68]:
(-128.0, -65.0, 23.0, 50.0)
In [ ]:
In [ ]:
In [23]:
Recycling_40209_1_plants.keys()
Out[23]:
Index(['plant type', 'location name', 'year', 'latitude (deg)',
       'longitude (deg)', 'capacity (tonne)', 'amount processed (tonne)',
       'amount received (tonne)', 'amount in storage (tonne)',
       'utilization factor (%)', 'energy (GJ)', 'opening cost ($)',
       'expansion cost ($)', 'fixed operating cost ($)',
       'variable operating cost ($)', 'storage cost ($)', 'total cost ($)'],
      dtype='object')
In [30]:
grid = sns.FacetGrid(Recycling_40209_1_plants, col="location name",
                     col_wrap=9, height=1.5)

# Draw a horizontal line to show the starting point
grid.refline(y=0, linestyle=":")


grid.map(plt.plot, "year", 'total cost ($)', color="powderblue")
grid.map(plt.fill_between, 'year', 'total cost ($)',color= "powderblue", alpha= 0.2)
# Adjust the tick positions, labels and 
#sns.set(font_scale=0.1)
grid.set_titles(row_template = '{row_name}', col_template = '{col_name}', size=5)

grid.fig.tight_layout(w_pad=1)
grid.fig.subplots_adjust(top=0.9)
grid.fig.suptitle(f"Title")


#plt.savefig(os.path.join(cwd, f"grid_after_update/grid_{files}_cap_uf.png"), transparent=True);
Out[30]:
Text(0.5, 0.98, 'Title')
In [58]:
usa.plot()

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In [58], line 1
----> 1 usa.plot()

NameError: name 'usa' is not defined
In [22]:
Recycling_40209_1_plants
Out[22]:
plant type location name year latitude (deg) longitude (deg) capacity (tonne) amount processed (tonne) amount received (tonne) amount in storage (tonne) utilization factor (%) energy (GJ) opening cost ($) expansion cost ($) fixed operating cost ($) variable operating cost ($) storage cost ($) total cost ($)
0 PV Recycling Plant Glen Lyn, Virginia 2025 37.318072 -80.698321 0.0 0.0 0.000000 0.000000 NaN 0.000000 0.0 0.0 0.000000 0.000000e+00 0.0 0.000000e+00
1 PV Recycling Plant Glen Lyn, Virginia 2026 37.318072 -80.698321 0.0 0.0 0.000000 0.000000 NaN 0.000000 0.0 0.0 0.000000 0.000000e+00 0.0 0.000000e+00
2 PV Recycling Plant Glen Lyn, Virginia 2027 37.318072 -80.698321 0.0 0.0 0.000000 0.000000 NaN 0.000000 0.0 0.0 0.000000 0.000000e+00 0.0 0.000000e+00
3 PV Recycling Plant Glen Lyn, Virginia 2028 37.318072 -80.698321 0.0 0.0 0.000000 0.000000 NaN 0.000000 0.0 0.0 0.000000 0.000000e+00 0.0 0.000000e+00
4 PV Recycling Plant Glen Lyn, Virginia 2029 37.318072 -80.698321 0.0 0.0 0.000000 0.000000 NaN 0.000000 0.0 0.0 0.000000 0.000000e+00 0.0 0.000000e+00
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2101 PV Recycling Plant Vanderbilt University Power Plant, Tennessee 2046 36.169129 -86.784790 7000.0 7000.0 6980.157642 1395.576712 100.0 6114.408689 0.0 0.0 236957.446809 2.388787e+06 0.0 2.625745e+06
2102 PV Recycling Plant Vanderbilt University Power Plant, Tennessee 2047 36.169129 -86.784790 7000.0 7000.0 5604.423288 0.000000 100.0 4909.306668 0.0 0.0 236957.446809 2.388787e+06 0.0 2.625745e+06
2103 PV Recycling Plant Vanderbilt University Power Plant, Tennessee 2048 36.169129 -86.784790 7000.0 7000.0 7000.000000 0.000000 100.0 6131.790000 0.0 0.0 236957.446809 2.388787e+06 0.0 2.625745e+06
2104 PV Recycling Plant Vanderbilt University Power Plant, Tennessee 2049 36.169129 -86.784790 7000.0 7000.0 7000.000000 0.000000 100.0 6131.790000 0.0 0.0 236957.446809 2.388787e+06 0.0 2.625745e+06
2105 PV Recycling Plant Vanderbilt University Power Plant, Tennessee 2050 36.169129 -86.784790 7000.0 7000.0 7000.000000 0.000000 100.0 6131.790000 0.0 0.0 236957.446809 2.388787e+06 0.0 2.625745e+06

2106 rows × 17 columns

In [ ]:

Overlapping locations¶

In [69]:
def intersection(lst1, lst2):
    lst3 = [value for value in lst1 if value in lst2]
    return lst3

Overlap between manufacturing and recycling¶

In [70]:
print('40209 overlap x0.5 cost:', len(intersection(Recycling_40209_05_plants['location name'].unique(), 
                                              Manufacturing_40209_05_plants['location name'].unique())))
print('40209 overlap x1 cost:', len(intersection(Recycling_40209_1_plants['location name'].unique(), 
                                              Manufacturing_40209_1_plants['location name'].unique())))
print('40209 overlap x2 cost:', len(intersection(Recycling_40209_2_plants['location name'].unique(), 
                                              Manufacturing_40209_2_plants['location name'].unique())))
print('NAICS overlap x0.5 cost:', len(intersection(Recycling_NAICS_05_plants['location name'].unique(), 
                                              Manufacturing_NAICS_05_plants['location name'].unique())))
print('NAICS overlap x1 cost:', len(intersection(Recycling_NAICS_1_plants['location name'].unique(), 
                                              Manufacturing_NAICS_1_plants['location name'].unique())))
print('NAICS overlap x2 cost:', len(intersection(Recycling_NAICS_2_plants['location name'].unique(), 
                                              Manufacturing_NAICS_2_plants['location name'].unique())))
print('40209 overlap x1 capacity:', len(intersection(Recycling_cap_40209_1_plants['location name'].unique(), 
                                              Manufacturing_cap_40209_1_plants['location name'].unique())))
print('40209 overlap x2 capacity:', len(intersection(Recycling_cap_40209_2_plants['location name'].unique(), 
                                              Manufacturing_cap_40209_2_plants['location name'].unique())))
print('NAICS overlap x1 capacity:', len(intersection(Recycling_cap_NAICS_1_plants['location name'].unique(), 
                                              Manufacturing_cap_NAICS_1_plants['location name'].unique()))),
print('NAICS overlap x2 capacity:', len(intersection(Recycling_cap_NAICS_2_plants['location name'].unique(), 
                                              Manufacturing_cap_NAICS_2_plants['location name'].unique())))

40209 overlap x0.5 cost: 72
40209 overlap x1 cost: 79
40209 overlap x2 cost: 81
NAICS overlap x0.5 cost: 79
NAICS overlap x1 cost: 81
NAICS overlap x2 cost: 81
40209 overlap x1 capacity: 79
40209 overlap x2 capacity: 37
NAICS overlap x1 capacity: 81
NAICS overlap x2 capacity: 37

Common locations to select specific cities¶

In [71]:
Recycling_40209_1_plants.keys()
Out[71]:
Index(['plant type', 'location name', 'year', 'latitude (deg)',
       'longitude (deg)', 'capacity (tonne)', 'amount processed (tonne)',
       'amount received (tonne)', 'amount in storage (tonne)',
       'utilization factor (%)', 'energy (GJ)', 'opening cost ($)',
       'expansion cost ($)', 'fixed operating cost ($)',
       'variable operating cost ($)', 'storage cost ($)', 'total cost ($)'],
      dtype='object')

40209 common locations¶

In [80]:
common_rec_40209 = intersection(
                       intersection(
                           intersection(
                               intersection(
                                   Recycling_40209_1_plants['location name'].unique(), 
                                   Recycling_40209_05_plants['location name'].unique()), 
                                   Recycling_40209_2_plants['location name'].unique()),
                                   Recycling_40209_5_plants['location name'].unique()),
                                   Recycling_40209_10_plants['location name'].unique())
In [86]:
common_rec_cap_40209 = intersection(
                       intersection(
                           intersection(
                               intersection(
                                   Recycling_cap_40209_1_plants['location name'].unique(), 
                                   Recycling_cap_40209_05_plants['location name'].unique()), 
                                   Recycling_cap_40209_2_plants['location name'].unique()),
                                   Recycling_cap_40209_5_plants['location name'].unique()),
                                   Recycling_cap_40209_10_plants['location name'].unique())
In [87]:
common_rec_cap_NAICS = intersection(
                       intersection(
                           intersection(
                               intersection(
                                   Recycling_cap_NAICS_1_plants['location name'].unique(), 
                                   Recycling_cap_NAICS_05_plants['location name'].unique()), 
                                   Recycling_cap_NAICS_2_plants['location name'].unique()),
                                   Recycling_cap_NAICS_5_plants['location name'].unique()),
                                   Recycling_cap_NAICS_10_plants['location name'].unique())
In [84]:
common_man_40209 = intersection(
                        intersection(
                            intersection(
                                Manufacturing_40209_0001_plants['location name'].unique(), 
                                Manufacturing_40209_05_plants['location name'].unique()), 
                                Manufacturing_40209_1_plants['location name'].unique()),
                                Manufacturing_40209_2_plants['location name'].unique())
In [81]:
common_rec_NAICS = intersection(
                       intersection(
                           intersection(
                               intersection(
                                   Recycling_NAICS_1_plants['location name'].unique(), 
                                   Recycling_NAICS_05_plants['location name'].unique()), 
                                   Recycling_NAICS_2_plants['location name'].unique()),
                                   Recycling_NAICS_5_plants['location name'].unique()),
                                   Recycling_NAICS_10_plants['location name'].unique())
In [93]:
common_man_cap_40209 = intersection( 
                                Manufacturing_cap_40209_1_plants['location name'].unique(),
                                Manufacturing_cap_40209_2_plants['location name'].unique())
In [ ]:
In [ ]:

Cost manufacturing

In [83]:
common_man_NAICS = intersection(
                        intersection(
                            intersection(
                                Manufacturing_NAICS_0001_plants['location name'].unique(), 
                                Manufacturing_NAICS_05_plants['location name'].unique()), 
                                Manufacturing_NAICS_1_plants['location name'].unique()),
                                Manufacturing_NAICS_2_plants['location name'].unique())

Capacity manufacturing

In [92]:
common_man_cap_NAICS = intersection( 
                                Manufacturing_cap_NAICS_1_plants['location name'].unique(),
                                Manufacturing_cap_NAICS_2_plants['location name'].unique())
In [ ]:
In [ ]:
In [ ]:
In [ ]: